Research Assistant (Wearable Foundation Model)
We aim to develop an advanced wearable foundation model for enabling personalized, continuous, and AI-driven health monitoring. We will employ multimodal wearable data collected from patients, including physiological signals such as ECG, PPG, accelerometery, and other biosensor measurements, to accurately model individual health status, disease progression, and functional changes over time. These models, coupled with machine learning and digital health technologies, will contribute to the identification of crucial physiological patterns and mechanistic relationships that offer insights into the trajectory of a patient’s condition. The research will explore the intersection of AI, wearable sensing, and biomedical sciences, developing novel approaches to transform continuous patient monitoring and personalized healthcare. With the potential to impact early diagnosis, disease management, and clinical decision-making, this project provides an exciting and important avenue for next-generation digital medicine.
The selected individual will be required to:
- Develop and validate novel wearable foundation models using Singapore cohort data.
- Design, implement, and benchmark deep learning models for multimodal physiological signal representation, disease prediction, and personalized health monitoring from wearable sensors.
- Collaborate with clinicians, data scientists, and engineers to translate AI-driven wearable technologies into actionable clinical insights and practical healthcare workflows.
- Publish research in top-tier journals and conferences and contribute to open-source software and reproducible tools for wearable foundation models.
Qualifications
- Possess at least a recognised degree in biomedical engineering, computer science, data science, applied statistics/ mathematics, or any-related field.
- Being self-motivated and enthusiastic about AI for healthcare.
- Strong problem-solving abilities and learning capability.
- Proficiency in programming (Python, C++, etc).
- Strong written and spoken communication skills.
- First-authored relative publication in top-tier journals and conferences are preferred.
- Open to fixed-term contract.
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